Defining Gene Regulatory Networks

A web app called GRNsight is the culmination of interdisciplinary collaboration among biology, computer science and mathematics faculty and students. GRNsight allows users to visualize models of gene regulatory networks (GRN) automatically. By studying these networks, biology Professor Kam Dahlquist hopes to “discover the basic rules that govern gene regulation.”

In particular, Dahlquist looks at how yeast cells respond to low temperature stress, an area that awaits deeper exploration. “Yeast is an important model for human cells. Many of the foundational things we know about cancer, we learned from yeast,” explains Dahlquist.

GRNsight produces an end product such as Figure 1, which shows the regulatory relationships between genes in the network, with the color and thickness of the line denoting the type and strength of the relationship. GRNsight translates spreadsheets of information generated by the mathematical modeling software GRNmap.

Figure 1

GRNsight builds on nearly a decade of partnership among Dahlquist, computer science Professor John David Dionisio, mathematics Professor Ben Fitzpatrick and their students.

In Dahlquist’s lab, she and her students observe what happens when yeast cells are exposed to different temperatures at varying time intervals. Collecting data on thousands of genes at a time, they then determine which genes behave differently at specific time intervals.

This data is input into the GRNmap mathematical modeling program written by Fitzpatrick and his team of interdisciplinary students, which produces spreadsheets that detail the dynamics of the gene regulatory network.

What do the spreadsheets say? That’s where GRNsight—programmed by Dionisio and his team of interdisciplinary students—comes in. The program translates spreadsheets of numbers into a diagram such as Figure 1 that users can adjust to best explain the network.

Collaboration like this doesn’t happen overnight, says Dahlquist, who works intimately with both Dionisio and Fitzpatrick in designing and testing the software. “There are significant start-up costs, but the payoff is incredible in the amount of synergy that we’ve built.”

To benefit the greater scientific community, the team of collaborators developed GRNsight (bit.ly/GRNsight) and GRNmap (bit.ly/LMUGRNmap) on open source platforms in a way that is user friendly for the average biologist.